Skip to content

Latest commit

 

History

History
574 lines (521 loc) · 37.4 KB

release_notes.rst

File metadata and controls

574 lines (521 loc) · 37.4 KB
Raw

Release Notes

Future Releases
  • Enhancements
  • Fixes
  • Changes
  • Documentation Changes
    • Fixed API docs for AutoMLSearch add_result_callback 1113
  • Testing Changes
v0.13.1 Aug. 25, 2020
  • Enhancements
    • Added Cost-Benefit Matrix objective for binary classification 1038
    • Split fill_value into categorical_fill_value and numeric_fill_value for Imputer 1019
    • Added explain_predictions and explain_predictions_best_worst for explaining multiple predictions with SHAP 1016
    • Added new LSA component for text featurization 1022
    • Added guide on installing with conda 1041
    • Added a “cost-benefit curve” util method to graph cost-benefit matrix scores vs. binary classification thresholds 1081
    • Standardized error when calling transform/predict before fit for pipelines 1048
    • Added percent_better_than_baseline to Automl search rankings and full rankings table 1050
    • Added one-way partial dependence and partial dependence plots 1079
    • Added "Feature Value" column to prediction explanation reports. 1064
    • Added LightGBM classification estimator 1082
    • Added max_batches parameter to AutoMLSearch 1087
  • Fixes
    • Updated TextFeaturizer component to no longer require an internet connection to run 1022
    • Fixed non-deterministic element of TextFeaturizer transformations 1022
    • Added a StandardScaler to all ElasticNet pipelines 1065
    • Updated cost-benefit matrix to normalize score 1099
    • Fixed logic in calculate_percent_difference so that it can handle negative values 1100
  • Changes
    • Added needs_fitting property to ComponentBase 1044
    • Updated references to data types to use datatype lists defined in evalml.utils.gen_utils 1039
    • Remove maximum version limit for SciPy dependency 1051
    • Moved all_components and other component importers into runtime methods 1045
    • Consolidated graphing utility methods under evalml.utils.graph_utils 1060
    • Made slight tweaks to how TextFeaturizer uses featuretools, and did some refactoring of that and of LSA 1090
    • Changed show_all_features parameter into importance_threshold, which allows for thresholding feature importance 1097, 1103
  • Documentation Changes
    • Update setup.py URL to point to the github repo 1037
    • Added tutorial for using the cost-benefit matrix objective 1088
  • Testing Changes
    • Refactor CircleCI tests to use matrix jobs (1043)
    • Added a test to check that all test directories are included in evalml package 1054

Warning

Breaking Changes
  • confusion_matrix and normalize_confusion_matrix have been moved to evalml.utils 1038
  • All graph utility methods previously under evalml.pipelines.graph_utils have been moved to evalml.utils.graph_utils 1060
v0.12.2 Aug. 6, 2020
  • Enhancements
    • Add save/load method to components 1023
    • Expose pickle protocol as optional arg to save/load 1023
    • Updated estimators used in AutoML to include ExtraTrees and ElasticNet estimators 1030
  • Fixes
  • Changes
    • Removed DeprecationWarning for SimpleImputer 1018
  • Documentation Changes
    • Add note about version numbers to release process docs 1034
  • Testing Changes
    • Test files are now included in the evalml package 1029
v0.12.0 Aug. 3, 2020
  • Enhancements
    • Added string and categorical targets support for binary and multiclass pipelines and check for numeric targets for DetectLabelLeakage data check 932
    • Added clear exception for regression pipelines if target datatype is string or categorical 960
    • Added target column names and class labels in predict and predict_proba output for pipelines 951
    • Added _compute_shap_values and normalize_values to pipelines/explanations module 958
    • Added explain_prediction feature which explains single predictions with SHAP 974
    • Added Imputer to allow different imputation strategies for numerical and categorical dtypes 991
    • Added support for configuring logfile path using env var, and don't create logger if there are filesystem errors 975
    • Updated catboost estimators' default parameters and automl hyperparameter ranges to speed up fit time 998
  • Fixes
    • Fixed ReadtheDocs warning failure regarding embedded gif 943
    • Removed incorrect parameter passed to pipeline classes in _add_baseline_pipelines 941
    • Added universal error for calling predict, predict_proba, transform, and feature_importances before fitting 969, 994
    • Made TextFeaturizer component and pip dependencies featuretools and nlp_primitives optional 976
    • Updated imputation strategy in automl to no longer limit impute strategy to most_frequent for all features if there are any categorical columns 991
    • Fixed UnboundLocalError for`cv_pipeline` when automl search errors 996
    • Fixed Imputer to reset dataframe index to preserve behavior expected from SimpleImputer 1009
  • Changes
    • Moved get_estimators to evalml.pipelines.components.utils 934
    • Modified Pipelines to raise PipelineScoreError when they encounter an error during scoring 936
    • Moved evalml.model_families.list_model_families to evalml.pipelines.components.allowed_model_families 959
    • Renamed DateTimeFeaturization to DateTimeFeaturizer 977
    • Added check to stop search and raise an error if all pipelines in a batch return NaN scores 1015
  • Documentation Changes
    • Update README.md 963
    • Reworded message when errors are returned from data checks in search 982
    • Added section on understanding model predictions with explain_prediction to User Guide 981
    • Added a section to the user guide and api reference about how XGBoost and CatBoost are not fully supported. 992
    • Added custom components section in user guide 993
    • Update FAQ section formatting 997
    • Update release process documentation 1003
  • Testing Changes
    • Moved predict_proba and predict tests regarding string / categorical targets to test_pipelines.py 972
    • Fix dependency update bot by updating python version to 3.7 to avoid frequent github version updates 1002

Warning

Breaking Changes
  • get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) 934
  • Removed the raise_errors flag in AutoML search. All errors during pipeline evaluation will be caught and logged. 936
  • evalml.model_families.list_model_families has been moved to evalml.pipelines.components.allowed_model_families 959
  • TextFeaturizer: the featuretools and nlp_primitives packages must be installed after installing evalml in order to use this component 976
  • Renamed DateTimeFeaturization to DateTimeFeaturizer 977
v0.11.2 July 16, 2020
  • Enhancements
    • Added NoVarianceDataCheck to DefaultDataChecks 893
    • Added text processing and featurization component TextFeaturizer 913, 924
    • Added additional checks to InvalidTargetDataCheck to handle invalid target data types 929
    • AutoMLSearch will now handle KeyboardInterrupt and prompt user for confirmation 915
  • Fixes
    • Makes automl results a read-only property 919
  • Changes
    • Deleted static pipelines and refactored tests involving static pipelines, removed all_pipelines() and get_pipelines() 904
    • Moved list_model_families to evalml.model_family.utils 903
    • Updated all_pipelines, all_estimators, all_components to use the same mechanism for dynamically generating their elements 898
    • Rename master branch to main 918
    • Add pypi release github action 923
    • Updated AutoMLSearch.search stdout output and logging and removed tqdm progress bar 921
    • Moved automl config checks previously in search() to init 933
  • Documentation Changes
    • Reorganized and rewrote documentation 937
    • Updated to use pydata sphinx theme 937
    • Updated docs to use release_notes instead of changelog 942
  • Testing Changes
    • Cleaned up fixture names and usages in tests 895

Warning

Breaking Changes
  • list_model_families has been moved to evalml.model_family.utils (previously was under evalml.pipelines.utils) 903
  • get_estimators has been moved to evalml.pipelines.components.utils (previously was under evalml.pipelines.utils) 934
  • Static pipeline definitions have been removed, but similar pipelines can still be constructed via creating an instance of PipelineBase 904
  • all_pipelines() and get_pipelines() utility methods have been removed 904
v0.11.0 June 30, 2020
  • Enhancements
    • Added multiclass support for ROC curve graphing 832
    • Added preprocessing component to drop features whose percentage of NaN values exceeds a specified threshold 834
    • Added data check to check for problematic target labels 814
    • Added PerColumnImputer that allows imputation strategies per column 824
    • Added transformer to drop specific columns 827
    • Added support for categories, handle_error, and drop parameters in OneHotEncoder 830 897
    • Added preprocessing component to handle DateTime columns featurization 838
    • Added ability to clone pipelines and components 842
    • Define getter method for component parameters 847
    • Added utility methods to calculate and graph permutation importances 860, 880
    • Added new utility functions necessary for generating dynamic preprocessing pipelines 852
    • Added kwargs to all components 863
    • Updated AutoSearchBase to use dynamically generated preprocessing pipelines 870
    • Added SelectColumns transformer 873
    • Added ability to evaluate additional pipelines for automl search 874
    • Added default_parameters class property to components and pipelines 879
    • Added better support for disabling data checks in automl search 892
    • Added ability to save and load AutoML objects to file 888
    • Updated AutoSearchBase.get_pipelines to return an untrained pipeline instance 876
    • Saved learned binary classification thresholds in automl results cv data dict 876
  • Fixes
    • Fixed bug where SimpleImputer cannot handle dropped columns 846
    • Fixed bug where PerColumnImputer cannot handle dropped columns 855
    • Enforce requirement that builtin components save all inputted values in their parameters dict 847
    • Don't list base classes in all_components output 847
    • Standardize all components to output pandas data structures, and accept either pandas or numpy 853
    • Fixed rankings and full_rankings error when search has not been run 894
  • Changes
    • Update all_pipelines and all_components to try initializing pipelines/components, and on failure exclude them 849
    • Refactor handle_components to handle_components_class, standardize to ComponentBase subclass instead of instance 850
    • Refactor "blacklist"/"whitelist" to "allow"/"exclude" lists 854
    • Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch 871
    • Renamed feature_importances and permutation_importances methods to use singular names (feature_importance and permutation_importance) 883
    • Updated automl default data splitter to train/validation split for large datasets 877
    • Added open source license, update some repo metadata 887
    • Removed dead code in _get_preprocessing_components 896
  • Documentation Changes
    • Fix some typos and update the EvalML logo 872
  • Testing Changes
    • Update the changelog check job to expect the new branching pattern for the deps update bot 836
    • Check that all components output pandas datastructures, and can accept either pandas or numpy 853
    • Replaced AutoClassificationSearch and AutoRegressionSearch with AutoMLSearch 871

Warning

Breaking Changes
  • Pipelines' static component_graph field must contain either ComponentBase subclasses or str, instead of ComponentBase subclass instances 850
  • Rename handle_component to handle_component_class. Now standardizes to ComponentBase subclasses instead of ComponentBase subclass instances 850
  • Renamed automl's cv argument to data_split 877
  • Pipelines' and classifiers' feature_importances is renamed feature_importance, graph_feature_importances is renamed graph_feature_importance 883
  • Passing data_checks=None to automl search will not perform any data checks as opposed to default checks. 892
  • Pipelines to search for in AutoML are now determined automatically, rather than using the statically-defined pipeline classes. 870
  • Updated AutoSearchBase.get_pipelines to return an untrained pipeline instance, instead of one which happened to be trained on the final cross-validation fold 876
v0.10.0 May 29, 2020
  • Enhancements
    • Added baseline models for classification and regression, add functionality to calculate baseline models before searching in AutoML 746
    • Port over highly-null guardrail as a data check and define DefaultDataChecks and DisableDataChecks classes 745
    • Update Tuner classes to work directly with pipeline parameters dicts instead of flat parameter lists 779
    • Add Elastic Net as a pipeline option 812
    • Added new Pipeline option ExtraTrees 790
    • Added precicion-recall curve metrics and plot for binary classification problems in evalml.pipeline.graph_utils 794
    • Update the default automl algorithm to search in batches, starting with default parameters for each pipeline and iterating from there 793
    • Added AutoMLAlgorithm class and IterativeAlgorithm impl, separated from AutoSearchBase 793
  • Fixes
    • Update pipeline score to return nan score for any objective which throws an exception during scoring 787
    • Fixed bug introduced in 787 where binary classification metrics requiring predicted probabilities error in scoring 798
    • CatBoost and XGBoost classifiers and regressors can no longer have a learning rate of 0 795
  • Changes
    • Cleanup pipeline score code, and cleanup codecov 711
    • Remove pass for abstract methods for codecov 730
    • Added __str__ for AutoSearch object 675
    • Add util methods to graph ROC and confusion matrix 720
    • Refactor AutoBase to AutoSearchBase 758
    • Updated AutoBase with data_checks parameter, removed previous detect_label_leakage parameter, and added functionality to run data checks before search in AutoML 765
    • Updated our logger to use Python's logging utils 763
    • Refactor most of AutoSearchBase._do_iteration impl into AutoSearchBase._evaluate 762
    • Port over all guardrails to use the new DataCheck API 789
    • Expanded import_or_raise to catch all exceptions 759
    • Adds RMSE, MSLE, RMSLE as standard metrics 788
    • Don't allow Recall to be used as an objective for AutoML 784
    • Removed feature selection from pipelines 819
    • Update default estimator parameters to make automl search faster and more accurate 793
  • Documentation Changes
    • Add instructions to freeze master on release.md 726
    • Update release instructions with more details 727 733
    • Add objective base classes to API reference 736
    • Fix components API to match other modules 747
  • Testing Changes
    • Delete codecov yml, use codecov.io's default 732
    • Added unit tests for fraud cost, lead scoring, and standard metric objectives 741
    • Update codecov client 782
    • Updated AutoBase __str__ test to include no parameters case 783
    • Added unit tests for ExtraTrees pipeline 790
    • If codecov fails to upload, fail build 810
    • Updated Python version of dependency action 816
    • Update the dependency update bot to use a suffix when creating branches 817

Warning

Breaking Changes
  • The detect_label_leakage parameter for AutoML classes has been removed and replaced by a data_checks parameter 765
  • Moved ROC and confusion matrix methods from evalml.pipeline.plot_utils to evalml.pipeline.graph_utils 720
  • Tuner classes require a pipeline hyperparameter range dict as an init arg instead of a space definition 779
  • Tuner.propose and Tuner.add work directly with pipeline parameters dicts instead of flat parameter lists 779
  • PipelineBase.hyperparameters and custom_hyperparameters use pipeline parameters dict format instead of being represented as a flat list 779
  • All guardrail functions previously under evalml.guardrails.utils will be removed and replaced by data checks 789
  • Recall disallowed as an objective for AutoML 784
  • AutoSearchBase parameter tuner has been renamed to tuner_class 793
  • AutoSearchBase parameter possible_pipelines and possible_model_families have been renamed to allowed_pipelines and allowed_model_families 793
v0.9.0 Apr. 27, 2020
  • Enhancements
    • Added accuracy as an standard objective 624
    • Added verbose parameter to load_fraud 560
    • Added Balanced Accuracy metric for binary, multiclass 612 661
    • Added XGBoost regressor and XGBoost regression pipeline 666
    • Added Accuracy metric for multiclass 672
    • Added objective name in AutoBase.describe_pipeline 686
    • Added DataCheck and DataChecks, Message classes and relevant subclasses 739
  • Fixes
    • Removed direct access to cls.component_graph 595
    • Add testing files to .gitignore 625
    • Remove circular dependencies from Makefile 637
    • Add error case for normalize_confusion_matrix() 640
    • Fixed XGBoostClassifier and XGBoostRegressor bug with feature names that contain [, ], or < 659
    • Update make_pipeline_graph to not accidentally create empty file when testing if path is valid 649
    • Fix pip installation warning about docsutils version, from boto dependency 664
    • Removed zero division warning for F1/precision/recall metrics 671
    • Fixed summary for pipelines without estimators 707
  • Changes
    • Updated default objective for binary/multiseries classification to log loss 613
    • Created classification and regression pipeline subclasses and removed objective as an attribute of pipeline classes 405
    • Changed the output of score to return one dictionary 429
    • Created binary and multiclass objective subclasses 504
    • Updated objectives API 445
    • Removed call to get_plot_data from AutoML 615
    • Set raise_error to default to True for AutoML classes 638
    • Remove unnecessary "u" prefixes on some unicode strings 641
    • Changed one-hot encoder to return uint8 dtypes instead of ints 653
    • Pipeline _name field changed to custom_name 650
    • Removed graphs.py and moved methods into PipelineBase 657, 665
    • Remove s3fs as a dev dependency 664
    • Changed requirements-parser to be a core dependency 673
    • Replace supported_problem_types field on pipelines with problem_type attribute on base classes 678
    • Changed AutoML to only show best results for a given pipeline template in rankings, added full_rankings property to show all 682
    • Update ModelFamily values: don't list xgboost/catboost as classifiers now that we have regression pipelines for them 677
    • Changed AutoML's describe_pipeline to get problem type from pipeline instead 685
    • Standardize import_or_raise error messages 683
    • Updated argument order of objectives to align with sklearn's 698
    • Renamed pipeline.feature_importance_graph to pipeline.graph_feature_importances 700
    • Moved ROC and confusion matrix methods to evalml.pipelines.plot_utils 704
    • Renamed MultiClassificationObjective to MulticlassClassificationObjective, to align with pipeline naming scheme 715
  • Documentation Changes
    • Fixed some sphinx warnings 593
    • Fixed docstring for AutoClassificationSearch with correct command 599
    • Limit readthedocs formats to pdf, not htmlzip and epub 594 600
    • Clean up objectives API documentation 605
    • Fixed function on Exploring search results page 604
    • Update release process doc 567
    • AutoClassificationSearch and AutoRegressionSearch show inherited methods in API reference 651
    • Fixed improperly formatted code in breaking changes for changelog 655
    • Added configuration to treat Sphinx warnings as errors 660
    • Removed separate plotting section for pipelines in API reference 657, 665
    • Have leads example notebook load S3 files using https, so we can delete s3fs dev dependency 664
    • Categorized components in API reference and added descriptions for each category 663
    • Fixed Sphinx warnings about BalancedAccuracy objective 669
    • Updated API reference to include missing components and clean up pipeline docstrings 689
    • Reorganize API ref, and clarify pipeline sub-titles 688
    • Add and update preprocessing utils in API reference 687
    • Added inheritance diagrams to API reference 695
    • Documented which default objective AutoML optimizes for 699
    • Create seperate install page 701
    • Include more utils in API ref, like import_or_raise 704
    • Add more color to pipeline documentation 705
  • Testing Changes
    • Matched install commands of check_latest_dependencies test and it's GitHub action 578
    • Added Github app to auto assign PR author as assignee 477
    • Removed unneeded conda installation of xgboost in windows checkin tests 618
    • Update graph tests to always use tmpfile dir 649
    • Changelog checkin test workaround for release PRs: If 'future release' section is empty of PR refs, pass check 658
    • Add changelog checkin test exception for dep-update branch 723

Warning

Breaking Changes

  • Pipelines will now no longer take an objective parameter during instantiation, and will no longer have an objective attribute.
  • fit() and predict() now use an optional objective parameter, which is only used in binary classification pipelines to fit for a specific objective.
  • score() will now use a required objectives parameter that is used to determine all the objectives to score on. This differs from the previous behavior, where the pipeline's objective was scored on regardless.
  • score() will now return one dictionary of all objective scores.
  • ROC and ConfusionMatrix plot methods via Auto(*).plot have been removed by 615 and are replaced by roc_curve and confusion_matrix in evamlm.pipelines.plot_utils in :pr:`704
  • normalize_confusion_matrix has been moved to evalml.pipelines.plot_utils 704
  • Pipelines _name field changed to custom_name
  • Pipelines supported_problem_types field is removed because it is no longer necessary 678
  • Updated argument order of objectives' objective_function to align with sklearn 698
  • pipeline.feature_importance_graph has been renamed to pipeline.graph_feature_importances in 700
  • Removed unsupported MSLE objective 704
v0.8.0 Apr. 1, 2020
  • Enhancements
    • Add normalization option and information to confusion matrix 484
    • Add util function to drop rows with NaN values 487
    • Renamed PipelineBase.name as PipelineBase.summary and redefined PipelineBase.name as class property 491
    • Added access to parameters in Pipelines with PipelineBase.parameters (used to be return of PipelineBase.describe) 501
    • Added fill_value parameter for SimpleImputer 509
    • Added functionality to override component hyperparameters and made pipelines take hyperparemeters from components 516
    • Allow numpy.random.RandomState for random_state parameters 556
  • Fixes
    • Removed unused dependency matplotlib, and move category_encoders to test reqs 572
  • Changes
    • Undo version cap in XGBoost placed in 402 and allowed all released of XGBoost 407
    • Support pandas 1.0.0 486
    • Made all references to the logger static 503
    • Refactored model_type parameter for components and pipelines to model_family 507
    • Refactored problem_types for pipelines and components into supported_problem_types 515
    • Moved pipelines/utils.save_pipeline and pipelines/utils.load_pipeline to PipelineBase.save and PipelineBase.load 526
    • Limit number of categories encoded by OneHotEncoder 517
  • Documentation Changes
    • Updated API reference to remove PipelinePlot and added moved PipelineBase plotting methods 483
    • Add code style and github issue guides 463 512
    • Updated API reference for to surface class variables for pipelines and components 537
    • Fixed README documentation link 535
    • Unhid PR references in changelog 656
  • Testing Changes
    • Added automated dependency check PR 482, 505
    • Updated automated dependency check comment 497
    • Have build_docs job use python executor, so that env vars are set properly 547
    • Added simple test to make sure OneHotEncoder's top_n works with large number of categories 552
    • Run windows unit tests on PRs 557

Warning

Breaking Changes

  • AutoClassificationSearch and AutoRegressionSearch's model_types parameter has been refactored into allowed_model_families
  • ModelTypes enum has been changed to ModelFamily
  • Components and Pipelines now have a model_family field instead of model_type
  • get_pipelines utility function now accepts model_families as an argument instead of model_types
  • PipelineBase.name no longer returns structure of pipeline and has been replaced by PipelineBase.summary
  • PipelineBase.problem_types and Estimator.problem_types has been renamed to supported_problem_types
  • pipelines/utils.save_pipeline and pipelines/utils.load_pipeline moved to PipelineBase.save and PipelineBase.load
v0.7.0 Mar. 9, 2020
  • Enhancements
    • Added emacs buffers to .gitignore 350
    • Add CatBoost (gradient-boosted trees) classification and regression components and pipelines 247
    • Added Tuner abstract base class 351
    • Added n_jobs as parameter for AutoClassificationSearch and AutoRegressionSearch 403
    • Changed colors of confusion matrix to shades of blue and updated axis order to match scikit-learn's 426
    • Added PipelineBase graph and feature_importance_graph methods, moved from previous location 423
    • Added support for python 3.8 462
  • Fixes
    • Fixed ROC and confusion matrix plots not being calculated if user passed own additional_objectives 276
    • Fixed ReadtheDocs FileNotFoundError exception for fraud dataset 439
  • Changes
    • Added n_estimators as a tunable parameter for XGBoost 307
    • Remove unused parameter ObjectiveBase.fit_needs_proba 320
    • Remove extraneous parameter component_type from all components 361
    • Remove unused rankings.csv file 397
    • Downloaded demo and test datasets so unit tests can run offline 408
    • Remove _needs_fitting attribute from Components 398
    • Changed plot.feature_importance to show only non-zero feature importances by default, added optional parameter to show all 413
    • Refactored PipelineBase to take in parameter dictionary and moved pipeline metadata to class attribute 421
    • Dropped support for Python 3.5 438
    • Removed unused apply.py file 449
    • Clean up requirements.txt to remove unused deps 451
    • Support installation without all required dependencies 459
  • Documentation Changes
    • Update release.md with instructions to release to internal license key 354
  • Testing Changes
    • Added tests for utils (and moved current utils to gen_utils) 297
    • Moved XGBoost install into it's own separate step on Windows using Conda 313
    • Rewind pandas version to before 1.0.0, to diagnose test failures for that version 325
    • Added dependency update checkin test 324
    • Rewind XGBoost version to before 1.0.0 to diagnose test failures for that version 402
    • Update dependency check to use a whitelist 417
    • Update unit test jobs to not install dev deps 455

Warning

Breaking Changes

  • Python 3.5 will not be actively supported.
v0.6.0 Dec. 16, 2019
  • Enhancements
    • Added ability to create a plot of feature importances 133
    • Add early stopping to AutoML using patience and tolerance parameters 241
    • Added ROC and confusion matrix metrics and plot for classification problems and introduce PipelineSearchPlots class 242
    • Enhanced AutoML results with search order 260
    • Added utility function to show system and environment information 300
  • Fixes
    • Lower botocore requirement 235
    • Fixed decision_function calculation for FraudCost objective 254
    • Fixed return value of Recall metrics 264
    • Components return self on fit 289
  • Changes
    • Renamed automl classes to AutoRegressionSearch and AutoClassificationSearch 287
    • Updating demo datasets to retain column names 223
    • Moving pipeline visualization to PipelinePlots class 228
    • Standarizing inputs as pd.Dataframe / pd.Series 130
    • Enforcing that pipelines must have an estimator as last component 277
    • Added ipywidgets as a dependency in requirements.txt 278
    • Added Random and Grid Search Tuners 240
  • Documentation Changes
    • Adding class properties to API reference 244
    • Fix and filter FutureWarnings from scikit-learn 249, 257
    • Adding Linear Regression to API reference and cleaning up some Sphinx warnings 227
  • Testing Changes
    • Added support for testing on Windows with CircleCI 226
    • Added support for doctests 233

Warning

Breaking Changes

  • The fit() method for AutoClassifier and AutoRegressor has been renamed to search().
  • AutoClassifier has been renamed to AutoClassificationSearch
  • AutoRegressor has been renamed to AutoRegressionSearch
  • AutoClassificationSearch.results and AutoRegressionSearch.results now is a dictionary with pipeline_results and search_order keys. pipeline_results can be used to access a dictionary that is identical to the old .results dictionary. Whereas, search_order returns a list of the search order in terms of pipeline_id.
  • Pipelines now require an estimator as the last component in component_list. Slicing pipelines now throws an NotImplementedError to avoid returning pipelines without an estimator.
v0.5.2 Nov. 18, 2019
  • Enhancements
    • Adding basic pipeline structure visualization 211
  • Documentation Changes
    • Added notebooks to build process 212
v0.5.1 Nov. 15, 2019
  • Enhancements
    • Added basic outlier detection guardrail 151
    • Added basic ID column guardrail 135
    • Added support for unlimited pipelines with a max_time limit 70
    • Updated .readthedocs.yaml to successfully build 188
  • Fixes
    • Removed MSLE from default additional objectives 203
    • Fixed random_state passed in pipelines 204
    • Fixed slow down in RFRegressor 206
  • Changes
    • Pulled information for describe_pipeline from pipeline's new describe method 190
    • Refactored pipelines 108
    • Removed guardrails from Auto(*) 202, 208
  • Documentation Changes
    • Updated documentation to show max_time enhancements 189
    • Updated release instructions for RTD 193
    • Added notebooks to build process 212
    • Added contributing instructions 213
    • Added new content 222
v0.5.0 Oct. 29, 2019
  • Enhancements
    • Added basic one hot encoding 73
    • Use enums for model_type 110
    • Support for splitting regression datasets 112
    • Auto-infer multiclass classification 99
    • Added support for other units in max_time 125
    • Detect highly null columns 121
    • Added additional regression objectives 100
    • Show an interactive iteration vs. score plot when using fit() 134
  • Fixes
    • Reordered describe_pipeline 94
    • Added type check for model_type 109
    • Fixed s units when setting string max_time 132
    • Fix objectives not appearing in API documentation 150
  • Changes
    • Reorganized tests 93
    • Moved logging to its own module 119
    • Show progress bar history 111
    • Using cloudpickle instead of pickle to allow unloading of custom objectives 113
    • Removed render.py 154
  • Documentation Changes
    • Update release instructions 140
    • Include additional_objectives parameter 124
    • Added Changelog 136
  • Testing Changes
    • Code coverage 90
    • Added CircleCI tests for other Python versions 104
    • Added doc notebooks as tests 139
    • Test metadata for CircleCI and 2 core parallelism 137
v0.4.1 Sep. 16, 2019
  • Enhancements
    • Added AutoML for classification and regressor using Autobase and Skopt 7 9
    • Implemented standard classification and regression metrics 7
    • Added logistic regression, random forest, and XGBoost pipelines 7
    • Implemented support for custom objectives 15
    • Feature importance for pipelines 18
    • Serialization for pipelines 19
    • Allow fitting on objectives for optimal threshold 27
    • Added detect label leakage 31
    • Implemented callbacks 42
    • Allow for multiclass classification 21
    • Added support for additional objectives 79
  • Fixes
    • Fixed feature selection in pipelines 13
    • Made random_seed usage consistent 45
  • Documentation Changes
    • Documentation Changes
    • Added docstrings 6
    • Created notebooks for docs 6
    • Initialized readthedocs EvalML 6
    • Added favicon 38
  • Testing Changes
    • Added testing for loading data 39
v0.2.0 Aug. 13, 2019
  • Enhancements
    • Created fraud detection objective 4
v0.1.0 July. 31, 2019
  • First Release
  • Enhancements
    • Added lead scoring objecitve 1
    • Added basic classifier 1
  • Documentation Changes
    • Initialized Sphinx for docs 1